Skip to main content
SpringerLink
Log in

A comparative analysis of computational drug repurposing approaches: proposing a novel tensor-matrix-tensor factorization method

  • Original Article
  • Published:
Molecular Diversity Aims and scope Submit manuscript

Abstract

Efficient drug discovery relies on drug repurposing, an important and open research field. This work presents a novel factorization method and a practical comparison of different approaches for drug repurposing. First, we propose a novel tensor-matrix-tensor (TMT) formulation as a new data array method with a gradient-based factorization procedure. Additionally, this paper examines and contrasts four computational drug repurposing approaches—factorization-based methods, machine learning methods, deep learning methods, and graph neural networks—to fulfill the second purpose. We test the strategies on two datasets and assess each approach’s performance, drawbacks, problems, and benefits based on results. The results demonstrate that deep learning techniques work better than other strategies and that their results might be more reliable. Ultimately, graph neural methods need to be in an inductive manner to have a reliable prediction.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Algorithm 1
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Data availability

The data and code are freely available at github.com/BioinformaticsIASBS/Tensor.

Materials availability

Not applicable.

References

  1. Denny JC, Collins FS (2021) Precision medicine in 2030—seven ways to transform healthcare. Cell 184(6):1415–1419. https://doi.org/10.1016/j.cell.2021.01.015

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Taylor K, Das S, Pearson M et al (2019) Systematic drug repurposing to enable precision medicine: a case study in breast cancer. Digital Medicine 5(4):180–186. https://doi.org/10.1016/j.cell.2021.01.015

    Article  CAS  Google Scholar 

  3. Bagherian M, Sabeti E, Wang K et al (2021) Machine learning approaches and databases for prediction of drug–target interaction: a survey paper. Brief Bioinform 22(1):247–269. https://doi.org/10.1093/bib/bbz157

    Article  PubMed  Google Scholar 

  4. To KK, Cho WC (2022) Drug repurposing for cancer therapy in the era of precision medicine. Curr Mol Pharmacol 15:895–903. https://doi.org/10.2174/1874467215666220214104530

    Article  CAS  Google Scholar 

  5. Lo YC (2016) Chemical dissection of the cell cycle for anticancer drug discovery and target identification. PhD thesis, UCLA

  6. Zhang R, Wang Z, Wang X et al (2023) Mhtan-dti: metapath-based hierarchical transformer and attention network for drug–target interaction prediction. Brief Bioinf 24(2):bbad079. https://doi.org/10.1093/bib/bbad079

    Article  CAS  Google Scholar 

  7. Chen H, Li J (2019) Modeling relational drug-target-disease interactions via tensor factorization with multiple web sources. In: The World Wide Web conference, pp 218–227. https://doi.org/10.1145/3308558.3313476

  8. von Davier AA, Wong PC, Polyak S, et al (2019) The argument for a “data cube” for large-scale psychometric data. In: Frontiers in Education, Frontiers Media SA, p 71. https://doi.org/10.3389/feduc.2019.00071

  9. Patil VA, Jayaswal DJ (2020) Context relevancy assessment in tensor factorization-based recommender systems. In: 2020 3rd international conference on communication system, computing and it applications (CSCITA), IEEE, pp 141–145. https://doi.org/10.1109/CSCITA47329.2020.9137778

  10. Yao J, Hurle MR, Nelson MR et al (2019) Predicting clinically promising therapeutic hypotheses using tensor factorization. BMC Bioinform 20(1):1–12. https://doi.org/10.1186/s12859-019-2664-1

    Article  Google Scholar 

  11. Ebied AMA (2019) Higher-order tensor decompositions for muscle synergy analysis. PhD thesis, The University of Edinburgh

  12. Turchetti C (2023) Decomposition of linear tensor transformations. https://doi.org/10.48550/arXiv.2309.07819

  13. Luo Y, Zhao X, Zhou J et al (2017) A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information. Nat Commun 8(1):1–13. https://doi.org/10.1038/s41467-017-00680-8

    Article  CAS  Google Scholar 

  14. Yamanishi Y, Araki M, Gutteridge A et al (2008) Prediction of drug–target interaction networks from the integration of chemical and genomic spaces. Bioinformatics 24(13):i232–i240. https://doi.org/10.1093/bioinformatics/btn162

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Wang YX, Zhang YJ (2012) Nonnegative matrix factorization: a comprehensive review. IEEE Trans Knowl Data Eng 25(6):1336–1353. https://doi.org/10.1109/TKDE.2012.51

    Article  CAS  Google Scholar 

  16. Cichocki A, Zdunek R, Si A (2007) Nonnegative matrix and tensor factorization [lecture notes]. IEEE Signal Process Mag 25(1):142–145. https://doi.org/10.1109/MSP.2008.4408452

    Article  Google Scholar 

  17. Acar E, Dunlavy DM, Kolda TG (2011) A scalable optimization approach for fitting canonical tensor decompositions. J Chemom 25(2):67–86. https://doi.org/10.1002/cem.1335

    Article  CAS  Google Scholar 

  18. Cichocki A, Mandic D, De Lathauwer L et al (2015) Tensor decompositions for signal processing applications: from two-way to multiway component analysis. IEEE Signal Process Mag 32(2):145–163. https://doi.org/10.1109/MSP.2013.2297439

    Article  Google Scholar 

  19. Papalexakis EE, Faloutsos C, Sidiropoulos ND (2016) Tensors for data mining and data fusion: models, applications, and scalable algorithms. ACM Trans Intell Syst Technol 8(2):1–44. https://doi.org/10.1145/2915921

    Article  Google Scholar 

  20. Ezzat A, Wu M, Li XL et al (2019) Computational prediction of drug—target interactions using chemogenomic approaches: an empirical survey. Brief Bioinform 20(4):1337–1357. https://doi.org/10.1093/bib/bby002

    Article  CAS  PubMed  Google Scholar 

  21. Chen R, Liu X, Jin S et al (2018) Machine learning for drug-target interaction prediction. Molecules 23(9):2208. https://doi.org/10.3390/molecules23092208

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Kim Y, Bismeijer T, Zwart W et al (2019) Genomic data integration by wonparafac identifies interpretable factors for predicting drug-sensitivity in vivo. Nat Commun 10(1):5034. https://doi.org/10.1038/s41467-019-13027-2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Martins RS, da Costa Gomes MF, Caffarena ER (2022) Combining network-based and matrix factorization to predict novel drug-target interactions: a case study using the brazilian natural chemical database. Curr Bioinform 17(9):793–803. https://doi.org/10.2174/1574893617666220820105258

    Article  CAS  Google Scholar 

  24. Tang X, Cai L, Meng Y et al (2021) Indicator regularized non-negative matrix factorization method-based drug repurposing for covid-19. Front Immunol. https://doi.org/10.3389/fimmu.2020.603615

    Article  PubMed  PubMed Central  Google Scholar 

  25. Guvenc Paltun B, Mamitsuka H, Kaski S (2021) Improving drug response prediction by integrating multiple data sources: matrix factorization, kernel and network-based approaches. Brief Bioinform 22(1):346–359. https://doi.org/10.1093/bib/bbz153

    Article  PubMed  Google Scholar 

  26. Kaya O, Ucar B (2018) Parallel candecomp/parafac decomposition of sparse tensors using dimension trees. SIAM J Sci Comput 40(1):C99–C130. https://doi.org/10.1137/16M1102744

    Article  Google Scholar 

  27. Uschmajew A, Vandereycken B (2013) The geometry of algorithms using hierarchical tensors. Linear Algebra App 439(1):133–166. https://doi.org/10.1016/j.laa.2013.03.016

    Article  Google Scholar 

  28. Acar E, Kolda TG, Dunlavy DM (2011) All-at-once optimization for coupled matrix and tensor factorizations. https://doi.org/10.48550/arXiv.1105.3422

  29. Acar E, Rasmussen MA, Savorani F et al (2013) Understanding data fusion within the framework of coupled matrix and tensor factorizations. Chemom Intell Lab Syst 129:53–63. https://doi.org/10.1016/j.chemolab.2013.06.006

    Article  CAS  Google Scholar 

  30. Balasubramaniam T, Nayak R, Yuen C (2019) Nonnegative coupled matrix tensor factorization for smart city spatiotemporal pattern mining. In: Machine learning, optimization, and data science: 4th international conference, LOD 2018, Volterra, Italy, September 13–16, 2018, Revised Selected Papers 4, Springer, pp. 520–532. https://doi.org/10.1007/978-3-030-13709-0 44

  31. Thirunavukarasu B, Nayak R, Yuen C (2020) Column-wise element selection for computationally efficient nonnegative coupled matrix tensor factorization. IEEE Trans Knowl Data Eng. https://doi.org/10.1109/TKDE.2020.2967045

    Article  Google Scholar 

  32. Acar E, Bro R, Smilde AK (2015) Data fusion in metabolomics using coupled matrix and tensor factorizations. Proc IEEE 103(9):1602–1620. https://doi.org/10.1109/JPROC.2015.2438719

    Article  CAS  Google Scholar 

  33. Mosayebi R, Hossein-Zadeh GA (2020) Correlated coupled matrix tensor factorization method for simultaneous eeg-fmri data fusion. Biomed Signal Process Control 62:102071. https://doi.org/10.1016/j.bspc.2020.102071

    Article  Google Scholar 

  34. Kingma DP, Ba J (2014) Adam: A method for stochastic optimization. Int Conf Learn Representations. Doi: https://doi.org/10.48550/arXiv.1412.6980

  35. Qi L, Hu S, Zhang X, et al (2019) Tensor norm, cubic power and gelfand limit. https://doi.org/10.48550/arXiv.1909.10942

  36. Liu S, Trenkler G et al (2008) Hadamard, Khatri-Rao, Kronecker and other matrix products. Int J Inf Syst Sciences 4(1):160–177

    Google Scholar 

  37. Han J, Kamber M, Pei J (2012) Data mining concepts and techniques, 3rd edn. Morgan Kaufmann

    Google Scholar 

  38. Hashemi SM, Zabihian A, Hooshmand M et al (2023) Draw: prediction of covid19 antivirals by deep learning—an objection on using matrix factorization. BMC Bioinform 24(1):52. https://doi.org/10.1186/s12859-023-05181-8

    Article  Google Scholar 

  39. Cover T, Hart P (1967) Nearest neighbor pattern classification. IEEE Trans Inf Theory 13(1):21–27. https://doi.org/10.1109/TIT.1967.1053964

    Article  Google Scholar 

  40. Chen T, Guestrin C (2016) Xgboost: a scalable tree boosting system. In: Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, pp 785–794. https://doi.org/10.1145/2939672.2939785

  41. Shi H, Liu S, Chen J et al (2019) Predicting drug-target interactions using lasso with random forest based on evolutionary information and chemical structure. Genomics 111(6):1839–1852. https://doi.org/10.1016/j.ygeno.2018.12.007

    Article  CAS  PubMed  Google Scholar 

  42. Keum J, Nam H (2017) SELF-BLM: prediction of drug-target interactions via self-training SVM. PLoS ONE 12(2):1–16. https://doi.org/10.1371/journal.pone.0171839

    Article  CAS  Google Scholar 

  43. Jarada TN, Rokne JG, Alhajj R (2020) A review of computational drug repositioning: strategies, approaches, opportunities, challenges, and directions. J Cheminf 12(1):1–23. https://doi.org/10.1186/s13321-020-00450-7

    Article  CAS  Google Scholar 

  44. Dinesh P, Kalyanasundaram P (2022) Medical image prediction for diagnosis of breast cancer disease comparing the machine learning algorithms: Svm, knn, logistic regression, random forest, and decision tree to measure accuracy. ECS Trans 107(1):12681. https://doi.org/10.1149/10701.12681ecst

    Article  CAS  Google Scholar 

  45. Pranckevicius T, Marcinkevicius V (2017) Comparison of Naive Bayes, random forest, decision tree, support vector machines, and logistic regression classifiers for text reviews classification. Baltic J Modern Comput 5(2):221. https://doi.org/10.22364/BJMC.2017.5.2.05

    Article  Google Scholar 

  46. Playe B, Stoven V (2020) Evaluation of deep and shallow learning methods in chemogenomics for the prediction of drugs specificity. J Cheminf 12(1):11. https://doi.org/10.1186/s13321-020-0413-0

    Article  Google Scholar 

  47. Beck BR, Shin B, Choi Y et al (2020) Predicting commercially available antiviral drugs that may act on the novel coronavirus (sars-cov-2) through a drug-target interaction deep learning model. Comput Struct Biotechnol J 18:784–790. https://doi.org/10.1016/j.csbj.2020.03.025

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Senior AW, Evans R, Jumper J et al (2020) Improved protein structure prediction using potentials from deep learning. Nature 577(7792):706–710. https://doi.org/10.1038/s41586-019-1923-7

    Article  CAS  PubMed  Google Scholar 

  49. Shin B, Park S, Kang K, et al (2019) Self-attention based molecule representation for predicting drug-target interaction. In: Machine learning for healthcare conference. PMLR, pp. 230–248

  50. Zeng X, Song X, Ma T et al (2020) Repurpose open data to discover therapeutics for covid-19 using deep learning. J Proteome Res 19(11):4624–4636. https://doi.org/10.1021/acs.jproteome.0c00316

    Article  CAS  PubMed  Google Scholar 

  51. Huang K, Xiao C, Glass LM et al (2021) MolTrans: molecular interaction transformer for drug-target interaction prediction. Bioinformatics 37(6):830–836. https://doi.org/10.1093/bioinformatics/btaa880

    Article  CAS  PubMed  Google Scholar 

  52. Kalakoti Y, Yadav S, Sundar D (2022) TransDTI: transformer-based language models for estimating DTIs and building a drug recommendation workflow. ACS Omega 7(3):2706–2717. https://doi.org/10.1021/acsomega.1c05203

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Bordes A, Usunier N, Garcia-Duran A, et al (2013) Translating embeddings for modeling multi-relational data. In: Advances in neural information processing systems

  54. Zabihian A, Sayyad FZ, Hashemi SM et al (2023) Dedti versus iedti: efficient and predictive models of drug-target interactions. Sci Rep 13(1):9238. https://doi.org/10.1038/s41598-023-36438-0

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. https://doi.org/10.48550/arXiv.1609.02907

  56. Hamilton WL (2020) Graph representation learning. Morgan & Claypool Publishers, California

    Book  Google Scholar 

  57. Yang Y, Su X, Zhao B et al (2023) Fuzzy-based deep attributed graph clustering. IEEE Trans Fuzzy Syst. https://doi.org/10.1109/TFUZZ.2023.3338565

    Article  Google Scholar 

  58. Wang L, Li ZW, You ZH et al (2023) Gslcda: an unsupervised deep graph structure learning method for predicting circrna-disease association. IEEE J Biomed Health Inform. https://doi.org/10.1109/JBHI.2023.3344714

    Article  PubMed  PubMed Central  Google Scholar 

  59. Wan F, Hong L, Xiao A et al (2019) Neodti: neural integration of neighbor information from a heterogeneous network for discovering new drug–target interactions. Bioinformatics 35(1):104–111. https://doi.org/10.1093/bioinformatics/bty543

    Article  CAS  PubMed  Google Scholar 

  60. Zhao T, Hu Y, Valsdottir LR et al (2021) Identifying drug–target interactions based on graph convolutional network and deep neural network. Brief Bioinform 22(2):2141–2150. https://doi.org/10.1093/bib/bbaa044

    Article  CAS  PubMed  Google Scholar 

  61. Peng J, Wang Y, Guan J et al (2021) An end-to-end heterogeneous graph representation learning-based framework for drug–target interaction prediction. Brief Bioinform 22(5):bbaa430. https://doi.org/10.1093/bib/bbaa430

    Article  CAS  PubMed  Google Scholar 

  62. Li J, Wang J, Lv H et al (2022) IMCHGAN: inductive matrix completion with heterogeneous graph attention networks for drug-target interactions prediction. IEEE/ACM Trans Comput Biol Bioinform 19(2):655–665. https://doi.org/10.1109/TCBB.2021.3088614

    Article  CAS  PubMed  Google Scholar 

  63. Rossi A, Tiezzi M, Dimitri GM, et al (2018) Inductive–transductive learning with graph neural networks. In: Artificial neural networks in pattern recognition: 8th IAPR TC3 workshop, ANNPR 2018, Siena, Italy, September 19–21, 2018, Proceedings 8, Springer, pp 201–212. https://doi.org/10.1109/TPAMI.2021.3054304

  64. Fu X, Zhang J, Meng Z et al (2020) Magnn: metapath aggregated graph neural network for heterogeneous graph embedding. Proc Web Conf 2020:2331–2341. https://doi.org/10.1145/3366423.3380297

    Article  Google Scholar 

  65. Tanha RS, Sadighian M, Zabihian A, et al (2023) Mad-ti: meta-path aggregatedgraph attention network for drug target interaction prediction. In: 2023 31st international conference on electrical engineering (ICEE), IEEE, pp 619–624. https://doi.org/10.1109/ICEE59167.2023.10334728

  66. Bottou L, Lin CJ et al (2007) Support vector machine solvers. Large Scale Kernel Machines 3(1):301–320. https://doi.org/10.7551/mitpress/7496.003.0003

    Article  Google Scholar 

  67. Velickovic P, Cucurull G, Casanova A, et al (2018) Graph attention networks. International Conference on Learning Representations. https://openreview.net/forum?id=rJXMpikCZ

  68. Chicco D, Jurman G (2020) The advantages of the matthews correlation coefficient (MCC) over f1 score and accuracy in binary classification evaluation. BMC Genomics 21(1):6. https://doi.org/10.1186/s12864-019-6413-7

    Article  PubMed  PubMed Central  Google Scholar 

  69. Fan J, Upadhye S, Worster A (2006) Understanding receiver operating characteristic (ROC) curves. Can J Emergency Med 8(1):19–20. https://doi.org/10.4097/kja.21209

    Article  Google Scholar 

  70. Teru KK, Denis EG, Hamilton WL (2020) Inductive relation prediction by subgraph reasoning. In: Proceedings of the 37th international conference on machine learning. JMLR.org, ICML’20

  71. Soh J, Park S, Lee H (2022) Hidti: integration of heterogeneous information to predict drug-target interactions. Sci Rep 12(1):1–12. https://doi.org/10.1038/s41598-022-07608-3

    Article  CAS  Google Scholar 

  72. Zhao BW, Su XR, Hu PW et al (2023) igrldti: an improved graph representation learning method for predicting drug–target interactions over heterogeneous biological information network. Bioinformatics 39(8):btad451. https://doi.org/10.1093/bioinformatics/btad451

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Jin J, Wang Y, Du K, et al (2022) Inductive relation prediction using analogy subgraph embeddings. In: International conference on learning representations. https://openreview.net/forum?id=PTRo58zPt3P

  74. Lee J, Chung C, Whang JJ (2023) Ingram: Inductive knowledge graph embedding via relation graphs. CoRR abs/2305.19987. https://doi.org/10.48550/ARXIV.2305.19987

  75. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273–297. https://doi.org/10.1007/BF00994018

    Article  Google Scholar 

  76. Ho TK (1995) Random decision forests. In: Proceedings of 3rd international conference on document analysis and recognition. IEEE, pp 278–282. https://doi.org/10.1109/ICDAR.1995.598994

Download references

Acknowledgements

The authors would like to thank Reza Shami Tanha for providing the results of MAD-TI.

Author information

Author notes

  1. Arash Zabihian and Javad Asghari have contributed equally to this work.

Authors and Affiliations

  1. Department of Bioinformatics, Kish International Campus, University of Tehran, Kish, Iran

    Arash Zabihian

  2. Department of Computer Science and Information Technology, Institute of Advanced Studies in Basic Sciences, Zanjan, Iran

    Javad Asghari & Mohsen Hooshmand

  3. Laboratory of Bioinformatics and Drug Design, University of Tehran, Tehran, Iran

    Sajjad Gharaghani

Authors
  1. Arash Zabihian
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Javad Asghari
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohsen Hooshmand
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sajjad Gharaghani
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

MH and AZ conceptualized the idea. JA implemented the methods. MH, AZ, and JA conceptualized the idea and wrote the manuscript. MH supervised and administered the project. SG proofread the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Mohsen Hooshmand.

Ethics declarations

Competing interest

The authors declare that they have no competing interests.

Ethics approval

Not applicable.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 85 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zabihian, A., Asghari, J., Hooshmand, M. et al. A comparative analysis of computational drug repurposing approaches: proposing a novel tensor-matrix-tensor factorization method. Mol Divers (2024). https://doi.org/10.1007/s11030-024-10851-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11030-024-10851-7

Keywords

Search

Navigation